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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1161–1170 of 2050 papers

TitleStatusHype
Exploring linguistic feature and model combination for speech recognition based automatic AD detection—0
Exploring the Potential of SSL Models for Sound Event Detection—0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
Face Recognition Using Deep Multi-Pose Representations—0
Face Recognition using Optimal Representation Ensemble—0
Factor-Augmented Regularized Model for Hazard Regression—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
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